Channel Estimation under Hardware Impairments: Bayesian Methods versus Deep Learning
This addresses channel estimation for wireless communication systems, but it is incremental as it compares deep learning with existing Bayesian methods.
The paper tackles channel estimation in the presence of hardware impairments in multiple-antenna systems, showing that a deep learning approach improves estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise.
This paper considers the impact of general hardware impairments in a multiple-antenna base station and user equipments on the uplink performance. First, the effective channels are analytically derived for distortion-aware receivers when using finite-sized signal constellations. Next, a deep feedforward neural network is designed and trained to estimate the effective channels. Its performance is compared with state-of-the-art distortion-aware and unaware Bayesian linear minimum mean-squared error (LMMSE) estimators. The proposed deep learning approach improves the estimation quality by exploiting impairment characteristics, while LMMSE methods treat distortion as noise.